This description relates to machine-learning, and more particularly, to methods and systems for using layered training in machine-learning architectures.
Known methods of online machine-learning receive streamed data to train models to conform to the streamed data. Additionally, other known methods of machine-learning receive batches of data to train models to conform to the received batches of data. At least some known systems distinguish streamed data based upon a time with which it is associated. Accordingly, when streamed data arrives late, a model may have been trained in the absence of the data. Therefore, in at least some known systems, latency in the arrival of streamed data requires refactoring, recalibration, or relearning for models. Such latency may further cause instability because late arriving data may cause significant adjustments in the model. In at least some known systems, it may be inefficient or impractical for the system to wait for all late-arriving data before training.